US20190114849A1 - Method for diagnosing noise cause of a vehicle - Google Patents

Method for diagnosing noise cause of a vehicle Download PDF

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Publication number
US20190114849A1
US20190114849A1 US15/825,673 US201715825673A US2019114849A1 US 20190114849 A1 US20190114849 A1 US 20190114849A1 US 201715825673 A US201715825673 A US 201715825673A US 2019114849 A1 US2019114849 A1 US 2019114849A1
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Prior art keywords
reference data
noise
sound source
vehicle
source signal
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Abandoned
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US15/825,673
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English (en)
Inventor
Dong Chul Lee
In Soo Jung
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hyundai Motor Co
Kia Corp
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Hyundai Motor Co
Kia Motors Corp
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Assigned to KIA MOTORS CORPORATION, HYUNDAI MOTOR COMPANY reassignment KIA MOTORS CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: JUNG, IN SOO, LEE, DONG CHUL
Publication of US20190114849A1 publication Critical patent/US20190114849A1/en
Abandoned legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H3/00—Measuring characteristics of vibrations by using a detector in a fluid
    • G01H3/04—Frequency
    • G01H3/08—Analysing frequencies present in complex vibrations, e.g. comparing harmonics present
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
    • G01H11/06—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/12—Testing internal-combustion engines by monitoring vibrations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/048—Marking the faulty objects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
    • G01N29/4427—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with stored values, e.g. threshold values
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/11—File system administration, e.g. details of archiving or snapshots
    • G06F16/116—Details of conversion of file system types or formats
    • G06F17/30076—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
    • B60Y2306/00—Other features of vehicle sub-units
    • B60Y2306/15—Failure diagnostics
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/10—Applications
    • G10K2210/128—Vehicles
    • G10K2210/1282—Automobiles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2499/00—Aspects covered by H04R or H04S not otherwise provided for in their subgroups
    • H04R2499/10—General applications
    • H04R2499/13—Acoustic transducers and sound field adaptation in vehicles

Definitions

  • the present disclosure relates to a method for easily diagnosing a cause of noise of a vehicle by comparing sound information generated in a vehicle with data in an artificial intelligence server.
  • the noise generated from the engine, the transmission, etc. mounted on a vehicle is diagnosed by a person by listening to the noise or by performing individual analysis after measuring the overall noise of the vehicle.
  • the present disclosure has been made in view of the above problems, and it is an object of the present disclosure to provide a method for diagnosing a cause of noise of a vehicle by analyzing, through an artificial intelligence server, a sound source signal received from a microphone provided in a vehicle and precisely identifying the cause of noise of the vehicle.
  • a method for diagnosing a cause of noise of a vehicle including receiving, by a controller, a sound source signal through a microphone installed in the vehicle, after the receiving, transmitting, by the controller, the received sound source signal to an artificial intelligence server and extracting, by the artificial intelligence server, reference data corresponding to the sound source signal from a pre-stored reference data map by comparing the received sound source signal with the reference data map, and after the extracting, transmitting, by the artificial intelligence server, the extracted reference data to the controller and outputting, by the controller, to a diagnostic apparatus, an output signal including information about the cause of noise of the vehicle based on the received reference data.
  • the microphone may be installed in an interior of the vehicle or on a side of an engine.
  • the extracting may include converting, by the artificial intelligence server, the received sound source signal into image data and comparing the converted image data with the reference data map to extract the corresponding reference data.
  • the artificial intelligence server may convert the sound source signal into the image data using a Gabor filter and a Mel filter.
  • the extracting may include converting, by the artificial intelligence server, the received sound source signal into a specific parameter using a neural network and comparing the converted specific parameter with the reference data map to extract the corresponding reference data.
  • the neural network may be a convolutional neural network (CNN) or a deep neural network (DNN) additionally using engine RPM (revolutions per minute) data.
  • CNN convolutional neural network
  • DNN deep neural network
  • engine RPM revolutions per minute
  • the extracting may include extracting, by the artificial intelligence server, the reference data using sound source information for an entire time of the received single sound source signal.
  • the extracting may include converting, by the artificial intelligence server, the received sound source signal into image data, converting the converted image data into a specific parameter using a neural network, and comparing the converted specific parameter with the reference data to extract the corresponding reference data.
  • the reference data may include information on a plurality of vehicle components causing noise and noise association ratio information on the vehicle components, wherein the outputting may include the controller outputting the output signal to the diagnostic apparatus such that the plurality of vehicle components is listed in descending order of noise association ratios based on the received reference data.
  • the cost and the labor cost rate required to identify the cause of noise when a problem occurs in the vehicle may be reduced.
  • the method enables average persons who do not have expertise to easily identify the cause of noise of a vehicle.
  • FIG. 1 is a flowchart illustrating a method for diagnosing a cause of noise of a vehicle according to an embodiment of the present application
  • FIG. 2 is a block diagram illustrating an apparatus for diagnosing a cause of noise of a vehicle according to an embodiment of the present application.
  • FIG. 3 illustrates operation of a diagnostic apparatus according to an embodiment of the present application.
  • FIG. 1 is a flowchart illustrating a method for diagnosing a cause of noise of a vehicle according to an embodiment of the present disclosure
  • FIG. 2 is a block diagram illustrating an apparatus for diagnosing a cause of noise of a vehicle according to an embodiment of the present invention.
  • a method for diagnosing a cause of noise of a vehicle may include a controller 100 receiving a sound source signal through a microphone 110 installed in the vehicle at step S 10 .
  • the controller 100 transmits the received sound source signal to an artificial intelligence server 120 .
  • the artificial intelligence server 120 compares the received sound source signal with a pre-stored reference data map and extracts reference data corresponding to the sound source signal from the reference data map at step S 20 .
  • the artificial intelligence server 120 transmits the extracted reference data to the controller 100 and the controller 100 outputs an output signal including information about the cause of noise of the vehicle based on the received reference data at step S 30 to a diagnostic apparatus 130 .
  • the controller 100 performs the reception step S 10 .
  • the microphone 110 is installed in the vehicle. Specifically, the microphone 110 may be installed in the interior of the vehicle or on the side of the engine. Therefore, the controller 100 may receive sound from the interior of the vehicle having a passenger riding therein, sound generated from the engine room, and the like, through the microphone 110 as sound source signals.
  • the controller 100 that has collected a sound source signal through the reception step S 10 transmits the sound source signal to the artificial intelligence server 120 .
  • the artificial intelligence server 120 compares the received sound source signal with a pre-stored reference data map and extracts reference data corresponding to the sound source signal.
  • the artificial intelligence server 120 collects noise data according to various failure situations, and classifies the same into deep learning-based big data types to secure a reference data map having a plurality of mapped reference data. Thereafter, when a sound source signal is received from the controller 100 , the artificial intelligence server 120 compares the sound source signal with the reference data map and extracts reference data having characteristics similar to that of a noise cause according to the sound source signal at S 20 .
  • the artificial intelligence server 120 may be provided in the form of a Web server such that the owner of the vehicle or the mechanic can easily access the server to diagnose the noise.
  • the controller 100 transmits the reference data to the controller 100 , and the controller 100 outputs noise cause information about the vehicle to the diagnostic apparatus 130 based on the received reference data, such that the owner of the vehicle or the mechanic can identify the cause of the noise of the vehicle through the diagnostic apparatus 130 .
  • the diagnostic apparatus 130 includes a display unit so that the driver or mechanic can identify the cause of the noise of the vehicle.
  • the information on the cause of the noise of the vehicle may be output to the display unit.
  • the artificial intelligence server 120 may convert the received sound source signal into image data, and then compare the converted image data with the reference data map to extract corresponding reference data.
  • the artificial intelligence server 120 may convert the sound source signal of a sound type into image data of an image type on the basis of time or frequency, and compare the feature vector representing the converted image data into the reference data map, thereby extracting reference data of the corresponding noise type.
  • the reference data stored in the artificial intelligence server 120 is provided in the form of images.
  • a specific noise may be extracted from the sound source signal, which is a mixture of various noise sources, to perform deep learning or to perform analysis by accurately comparing the noise with the corresponding reference data.
  • the artificial intelligence server 120 may convert the sound source signal into image data using a Gabor filter and a Mel filter.
  • the artificial intelligence server 120 may convert the received sound source signal into a specific parameter using a neural network, and then compare the converted specific parameter with the reference data, thereby extracting corresponding reference data.
  • the neural network may be a convolutional neural network (CNN) or a deep neural network (DNN) or additionally using engine RPM data.
  • CNN convolutional neural network
  • DNN deep neural network
  • the DNN and the CNN are neural networks that improve accuracy of artificial intelligence machine learning.
  • the DNN and the CNN time/frequency-filter sound source signals and then classify the same into noise types by specific parameters such as vehicle location or vehicle component.
  • the artificial intelligence server 120 may distinguish between various types of noise from the sound source signal by extracting reference data corresponding to the converted specific parameters and perform comparison and analysis, thereby improving the discrimination power and accuracy of analysis of the cause of the vehicle noise.
  • the neural networks may further discriminate the characteristics of a noise source resulting from the revolutions per minute (RPM) of the engine from a noise source which does not result from the RPM by additionally applying the engine RPM information in order to reflect a special condition for distinguishing between the vehicle noise sources. Therefore, the accuracy of noise source classification in the vehicle may be improved.
  • RPM revolutions per minute
  • the artificial intelligence server 120 may extract the reference data using the sound source information for the entire time of the received single sound source signal.
  • the present technology may improve the accuracy of the learning model and accurately extract the reference data by applying a learning algorithm using long-term learning of the entirety of one sound source signal. Therefore, both the noise source generated in a short time and the noise source generated in a long time may be learned.
  • the artificial intelligence server 120 may convert the received sound source signal into image data, convert the converted image data into a specific parameter using a neural network, and then compare the converted specific parameter with the reference data to extract the corresponding reference data.
  • the artificial intelligence server 120 since the artificial intelligence server 120 performs conversion of the sound source signal received from the controller 100 in two steps and then compares the sound source signal with the reference data for analysis, the artificial intelligence server 120 may accurately distinguish between the noise types based on the feature vectors of the image data and have the advantage of the neural network for distinguishing between various types of noise. Therefore, the cause of noise of the vehicle may be diagnosed accurately and distinguishably.
  • the reference data may include information on a plurality of vehicle components that cause noise and noise association ratio information on the vehicle components.
  • the controller 100 may output an output signal to the diagnostic apparatus 130 such that the plurality of vehicle components is listed in descending order of noise association ratios based on the received reference data.
  • FIG. 3 illustrates operation of a diagnostic apparatus 130 according to one embodiment.
  • the controller generates an output signal based on the reference data received from the artificial intelligence server, and transmits the same to output, through the display unit of the diagnostic apparatus 130 , information indicating whether the noise of the vehicle corresponds to the noise of specific vehicle components and the noise association ratio of the corresponding vehicle components.
  • the reference data includes a vehicle location causing noise and noise association ratio information about the location, in addition to information on a plurality of vehicle components causing noise and noise association ratio information.
  • the owner of the vehicle or a mechanic may easily identify which of the plurality of vehicle components is related to the cause of the noise by checking the display unit of the diagnostic apparatus 130 .
  • the cost and the labor cost rate required to identify the cause of noise when a problem occurs in the vehicle may be reduced.
  • the method enables average persons who do not have expertise to easily identify the cause of noise of a vehicle.
US15/825,673 2017-10-16 2017-11-29 Method for diagnosing noise cause of a vehicle Abandoned US20190114849A1 (en)

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KR1020170133858A KR102324776B1 (ko) 2017-10-16 2017-10-16 차량의 소음원인 진단방법
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US20200118358A1 (en) * 2018-10-11 2020-04-16 Hyundai Motor Company Failure diagnosis method for power train components
US20200184991A1 (en) * 2018-12-05 2020-06-11 Pascal Cleve Sound class identification using a neural network
US10943486B2 (en) * 2018-11-29 2021-03-09 Hyundai Motor Company Traveling safety control system using ambient noise and control method thereof
CN113218495A (zh) * 2021-04-30 2021-08-06 马勒汽车技术（中国）有限公司 发动机的活塞噪声的测试方法及装置
CN113447274A (zh) * 2020-03-24 2021-09-28 本田技研工业株式会社 异常声音判定装置以及异常声音判定方法
US20210302269A1 (en) * 2020-03-26 2021-09-30 Toyota Jidosha Kabushiki Kaisha Method of specifying location of occurrence of abnormal sound, non-transitory storage medium, and in-vehicle device
EP3896408A1 (en) * 2020-04-16 2021-10-20 Toyota Jidosha Kabushiki Kaisha Abnormal noise evaluation system and abnormal noise evaluation method
US11494643B2 (en) 2018-12-13 2022-11-08 Hyundai Motor Company Noise data artificial intelligence apparatus and pre-conditioning method for identifying source of problematic noise
US11521435B2 (en) 2018-12-12 2022-12-06 Hyundai Motor Company Method and device for diagnosing problematic noise source based on big data information
US11593610B2 (en) * 2018-04-25 2023-02-28 Metropolitan Airports Commission Airport noise classification method and system
US11741761B2 (en) 2020-01-06 2023-08-29 Hyundai Motor Company State diagnosis apparatus and method of moving system part
US11775820B2 (en) 2019-12-11 2023-10-03 Hyundai Motor Company Information sharing platform and method capable of providing bidirectional vehicle state information and system having information sharing platform
US11780442B2 (en) 2020-12-23 2023-10-10 Toyota Jidosha Kabushiki Kaisha Sound source estimation server, sound source estimation system, sound source estimation device, and sound source estimation method

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US11593610B2 (en) * 2018-04-25 2023-02-28 Metropolitan Airports Commission Airport noise classification method and system
US20200118358A1 (en) * 2018-10-11 2020-04-16 Hyundai Motor Company Failure diagnosis method for power train components
US10943486B2 (en) * 2018-11-29 2021-03-09 Hyundai Motor Company Traveling safety control system using ambient noise and control method thereof
US20200184991A1 (en) * 2018-12-05 2020-06-11 Pascal Cleve Sound class identification using a neural network
US11521435B2 (en) 2018-12-12 2022-12-06 Hyundai Motor Company Method and device for diagnosing problematic noise source based on big data information
US11494643B2 (en) 2018-12-13 2022-11-08 Hyundai Motor Company Noise data artificial intelligence apparatus and pre-conditioning method for identifying source of problematic noise
US11775820B2 (en) 2019-12-11 2023-10-03 Hyundai Motor Company Information sharing platform and method capable of providing bidirectional vehicle state information and system having information sharing platform
US11741761B2 (en) 2020-01-06 2023-08-29 Hyundai Motor Company State diagnosis apparatus and method of moving system part
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CN113447274A (zh) * 2020-03-24 2021-09-28 本田技研工业株式会社 异常声音判定装置以及异常声音判定方法
US11566966B2 (en) * 2020-03-26 2023-01-31 Toyota Jidosha Kabushiki Kaisha Method of specifying location of occurrence of abnormal sound, non-transitory storage medium, and in-vehicle device
US20210302269A1 (en) * 2020-03-26 2021-09-30 Toyota Jidosha Kabushiki Kaisha Method of specifying location of occurrence of abnormal sound, non-transitory storage medium, and in-vehicle device
EP3896408A1 (en) * 2020-04-16 2021-10-20 Toyota Jidosha Kabushiki Kaisha Abnormal noise evaluation system and abnormal noise evaluation method
US11749033B2 (en) 2020-04-16 2023-09-05 Toyota Jidosha Kabushiki Kaisha Abnormal noise evaluation system and abnormal noise evaluation method
US11780442B2 (en) 2020-12-23 2023-10-10 Toyota Jidosha Kabushiki Kaisha Sound source estimation server, sound source estimation system, sound source estimation device, and sound source estimation method
CN113218495A (zh) * 2021-04-30 2021-08-06 马勒汽车技术（中国）有限公司 发动机的活塞噪声的测试方法及装置

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KR102324776B1 (ko) 2021-11-10
DE102017221701A1 (de) 2019-04-18
DE102017221701B4 (de) 2023-10-05

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